AI growth demands massive investment, primarily from businesses
A new analysis from the Boston Consulting Group (BCG) Institute projects that the artificial intelligence ecosystem will require approximately $2.6 trillion annually by 2030 to cover expenses related to data centers, specialized chips, model development, and returns for investors.
According to the report, businesses are expected to contribute the largest portion of this investment, totaling $1.8 trillion. Consumers will account for $366 billion, while hyperscalers’ internal usage will require $204 billion, and emerging AI technologies will need $200 billion. These figures represent global spending, excluding China, and largely reflect infrastructure development concentrated in the United States.
The $1.8 trillion figure represents the necessary investment from businesses, not a prediction of actual spending. BCG suggests companies could meet this need by reallocating $857 billion from existing technology budgets and adding $937 billion in new spending. This would increase enterprise technology budgets by roughly 13% above current forecasts, with AI’s share rising from 5% to 25% by 2030.
Clark O’Niell, BCG’s global lead for AI, infrastructure, and cyber, emphasized the importance of demonstrating productivity gains, cost reductions, or competitive advantages to secure AI investment approvals from corporate boards. He cautioned that while the fear of falling behind can be a strategic consideration, it shouldn’t replace a solid business case.
BCG believes this AI build-out is more stable than previous infrastructure booms, partly due to hyperscalers financing investments through cash flow. However, generating adequate returns remains crucial. O’Niell highlighted monitoring infrastructure financing methods and the revenue generated by deployed compute as key indicators.
The analysis also notes that AI chips are assumed to have a five-year lifespan, and each gigawatt of capacity coming online in 2026 needs to generate approximately $15 billion in revenue to cover costs. Lower-than-expected demand could lead to price competition and losses. O’Niell advises companies to avoid long-term price commitments due to anticipated price declines, suggesting contract flexibility for CFOs to capitalize on future cost reductions. The $2.6 trillion goal, while achievable, is not guaranteed, and the question remains whether businesses will realize sufficient economic value to justify the investment.
Source
“Who pays for AI’s $2.6 trillion build-out? Look to corporate budgets”
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